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  • Your Trusted Partner in Rapid Test Products

    Dear info,

     

    Hope this message finds you well.

     

    I’m reaching out from Core Technology Co., Ltd., a subsidiary of the American Cordx Union group, with over a decade of expertise in medical diagnostics. Our rapid test products, such as HCG Pregnancy Tests, LH Ovulation Tests, and Influenza A/B + COVID-19 Ag Combo Tests, are used by millions worldwide across 170+ countries.

     

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    If this aligns with your needs, I would be happy to discuss how we can support your market goals.

     

    Best regards,

    Ashely

    Core Technology Co., Ltd.

    http://www.coretests.com

     

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    by "Urni Nikuda" <nikudaurni@gmail.com> - 03:41 - 17 Apr 2025
  • shock absorber

    Dear info,

     

    Greetings from Henghong Intelligent Equipment Co.,Ltd!

    We are professional in R&D,production and sales of automotive shock absorbers.Our team has explored your website and noticed significant synergies between our product lines.With in-depth knowledge of your market,we are eager to share valuable insights and explore potential collaborations with you.

    Would you like to accept our catalogue?It could open up new avenues for common development.

     

    Best Regards!

    Echo Gao


    by "sales5" <sales5@henghongparts.com> - 12:40 - 17 Apr 2025
  • Coupling and Cohesion: The Two Principles for Effective Architecture

    Coupling and Cohesion: The Two Principles for Effective Architecture

    Every large system that spirals out of control starts the same way: small, functional, and deceptively simple.
    ͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­
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    Every large system that spirals out of control starts the same way: small, functional, and deceptively simple. However, as the system evolves, things spiral out of control.

    A feature is added here, a helper function squeezed there, and a “temporary” dependency for some urgent task that never gets removed. Months later, debugging requires going through five layers of indirection, and touching one module can break the entire system. 

    Behind the scenes of that slow collapse, two invisible forces often play tug-of-war: coupling and cohesion.

    Most developers first hear these terms in textbooks or blog posts, often lumped into a “good design” checklist. 

    • High cohesion: good. 

    • Loose coupling: also good. 

    But beyond the concepts, the practical meaning often gets lost. What does coupling look like? When does cohesion break down in real teams? And why do some projects feel like a breeze to change, while others offer challenges with every pull request?

    Coupling and cohesion aren’t abstract guidelines. They are practical engineering realities that define how easily code could evolve, how confidently teams could deploy, and how painful it becomes to onboard a new teammate or fix a bug under pressure.

    In this article, we’ll attempt to understand coupling and cohesion in more realistic terms and how they might show up in different architectural styles and patterns.

    Understanding Coupling...

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    by "ByteByteGo" <bytebytego@substack.com> - 11:35 - 17 Apr 2025
  • Quick estimate
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    Do you have any estimation jobs for us?
    
    If so, please share the drawings or illustrations, and we’ll provide you with a quote. Once approved, we can proceed further.
    
    Feel free to reach out if you have any questions.Thank you
    
    Looking forward to working with you.
    
    Kyle Edison

    by kyle.hamestimating@gmail.com - 10:21 - 17 Apr 2025
  • Too many resumes. Not enough hires.

    Too many resumes. Not enough hires.

    Hi MD,

    If your hiring pipeline feels overloaded — but still underwhelming — you’re not alone.

     

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    by "Gillian at Remote" <hello@remote-comms.com> - 10:09 - 17 Apr 2025
  • Upcoming Tyk webinars: AI readiness & 5.8 release

    Upcoming Tyk webinars: AI readiness & 5.8 release

    Hi Md Abul,

    With 2025 in full-swing, here’s a reminder of our upcoming webinars for you:

    AI, APIs, and the path to AI-readiness: Structuring the AI supply chain for enterprise adoption

     

    📅 Date: April 24, 2025
    ⏰ Time: 10 AM EST / 3 PM BST
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    Join us to explore the new capabilities of Tyk 5.8 and how they can amplify OpenAPI-powered API definitions, enhancing security, governance, and workflows.

    Tyk 5.8: OAS-native API management for secure, interoperable and governance-first API experience 

     

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    Want to stay in the loop of more events for the API community? Follow us on social media below.

     

    Got queries about events? Contact us at communities@tyk.io

     

    See you there!

     

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    by "Budhaditya Bhattacharya" <budha@tyk.io> - 10:04 - 17 Apr 2025
  • would you like to check free samples of solar cables and connectors--slocable joyce

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    by "market9" <market9@slocablevip.com> - 09:12 - 17 Apr 2025
  • Premium Lamination & Silicon Cores Available

    Dear Manager,


    I am Sandy from Jiangyin Chuangjia Electrical Co., Ltd. We are a leading manufacturer of transformer cores (EI/UI/By drawing), lamination cores, and silicon cores. 

     

    We are interested in exploring potential collaboration opportunities and providing you with high-quality products tailored to your needs.

     

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    Company Name: JIANGYIN CHUANGJIA ELECTRICAL CO., LTD                                                
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    Industry, Type of products: Silicon steel core, transformer core,motor lamination(stator&rotor),CRGO/CRNO slit coils.                                               
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    Jiangyin Chuangjia Electrical Co.,ltd
    Web:www.siliconsteelcore.com
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    by "sales15" <sales15@stampingcore.com> - 06:38 - 17 Apr 2025
  • You're Invited! Product Expert Series: Intelligent Observability for Business Uptime

    New Relic



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    Join us for our Product Expert Series: Intelligent Observability for Business Uptime.

    This is your chance for a live demo and Q&A with the team behind agentic integrations, predictions, response intelligence, and more innovations for minimizing downtime. Let them show you how you can:

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    by "New Relic" <emeamarketing@newrelic.com> - 04:59 - 17 Apr 2025
  • Join us for the Agentforce World Tour Broadcast | 29 May

    Join us for the Agentforce World Tour Broadcast | 29 May

    Discover Agentforce, the Digital Labour Platform at Salesforce
    Discover Agentforce, the Digital Labour Platform at Salesforce
    AWT Broadcast

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    29 May 2025
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  • How can leaders tackle expanding trade controls and tariffs?

    On McKinsey Perspectives

    Why your business needs a nerve center ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ 
    McKinsey & Company
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  • High-Quality Valves for Industrial Applications

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    Dear Company,

     

    I am writing this email as a foreign trade salesman of Bogong Valve Co., Ltd. We are a valve company located in Wenzhou, Zhejiang, China, specializing in manufacturing various types of valves, such as gate valves, check valves, globe valves, butterfly valves, and ball valves. With more than two decades of manufacturing experience, we have API, EAC, and TS certifications, ensuring that we are at the forefront of the manufacturing industry. Our valves are of high quality and reasonable price, and enjoy a high reputation in China.

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    by "sales03" <sales03@BogongValves.com> - 07:31 - 16 Apr 2025
  • Introduction of HISAN: Your trusted partners for Weld Overlay Wear Plates

    Dear info,

     

    I hope this message finds you well.

     

    I am writing to introduce our esteemed company, HISAN (Tianjin) Co., Ltd, an ISO9001 certified manufacturer specializing in high-quality weld overlay wear plates. Our extensive range of products includes various grades and thicknesses, meticulously designed to cater to diverse working conditions.

     

    Our facility, covering 11,000 square meters, boasts a monthly production capacity of 12 containers of wear plates. Among our offerings, the Grade HS200 plates have garnered significant popularity due to their exceptional quality and performance.

     

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    HISAN(Tianjin) Co., Ltd

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    by "Kiyo Harscher" <blaisegudd501@gmail.com> - 12:44 - 16 Apr 2025
  • Achilles Coaching Sport Santé , vous utilisez quoi pour la musique ?

    Bonjour,


    Une question rapide : comment gérez-vous l’ambiance musicale chez Achilles Coaching Sport Santé ?


    Beaucoup de commerces utilisent des radios FM ou Spotify, mais cela pose parfois des problèmes : publicité, musique pas adaptée ou frais cachés.


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    Qu’en diriez-vous d’en discuter ? 😊


    Romain

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    by "Romain Rigaud" <romain.rigaud@license-jamendo.fr> - 08:36 - 16 Apr 2025
  • RE: Looking for a Professional Mexican Agent

    Dear agent,

     

    Greetings from Crystal and the entire GLA family. We hope this message finds you well.

     

    I would like to extend a sincere invitation to you to become a part of the GLA platform. As we experience a growing demand from/to Mexico among our GLA members, your participation would be highly valuable in helping us meet and address these requirements effectively.

     

                                                                                    

     

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    Notice Agreement No 7

    7. GLA president reserves the right to cancel or reject membership or application. Company shall cease to be a member of GLA if:

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    d) Have bad debt records in the GLA platform or in the market

     

     


    by "Crystal LEE" <member72@glafamily.com> - 08:08 - 16 Apr 2025
  • How global companies can succeed in India

    On McKinsey Perspectives

    What leaders should know ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌   ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ 
    McKinsey & Company
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    Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.

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    by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:42 - 16 Apr 2025
  • Customize your safety helmet to meet market demand!

    Dear info,

     

    Hello! We specialize in designing and manufacturing all kinds of helmets, including ski helmets, bicycle helmets, climbing helmets, baseball helmets, etc. We provide customers with a full range of OEM and ODM services, and can provide customized solutions according to your specific needs to ensure that each helmet meets the expectations of the market and users.

     

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    by "Kristof Fabiana" <fabianakristof707@gmail.com> - 10:01 - 15 Apr 2025
  • Join Us at Stratosphere 2025 – The Future of Digital Transformation Starts Here

    Join Us at Stratosphere 2025 – The Future of Digital Transformation Starts Here

                
                
    image001

    Hi MD Abul,

    We’re excited to officially open registration for Stratosphere 2025, taking place August 25–27, 2025 in Denver, Colorado!

    What is Digital Stratosphere?
    It’s more than just another tech conference. Stratosphere 2025 is where the world’s leading digital transformation experts, CIOs, and change agents come together to explore enterprise technology strategies independent of vendors and sales pitches.

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  • How Netflix Orchestrates Millions of Workflow Jobs with Maestro

    How Netflix Orchestrates Millions of Workflow Jobs with Maestro

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    Disclaimer: The details in this post have been derived from the articles written by the Netflix engineering team. All credit for the technical details goes to the Netflix Engineering Team. The links to the original articles and videos are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.

    Netflix originally used an orchestrator named Meson to manage its growing number of data and machine learning (ML) workflows. 

    For several years, Meson served the company well. It was responsible for scheduling and managing a large volume of tasks, approximately 70,000 workflows and 500,000 daily jobs. These jobs supported various business functions, including data transformation, recommendation models, A/B tests, and other ML-based decision systems.

    However, as Netflix’s use of data grew, so did the pressure on Meson. The system began to show signs of strain, especially during periods of peak traffic, such as midnight UTC, when a large number of workflows were typically triggered. During these times, the orchestrator experienced slowdowns that led to increased operational overhead. Engineers on call had to closely monitor the system, especially during off-hours, to ensure it didn’t fail under the load.

    A key limitation of Meson was its architecture. It was built using a single-leader model, which meant there was one main node responsible for coordinating all activity. Although it had high availability, the team had to vertically scale (i.e., upgrade to more powerful machines) to keep up with the growing demand. Eventually, they approached the limits of what AWS instance types could offer, making it clear that vertical scaling was not a sustainable long-term solution.

    At the same time, the rate of growth was dramatic. The number of workflows in the system was doubling year over year, which added to the urgency of finding a better solution.

    This need led to the creation of Maestro: a next-generation workflow orchestrator designed to overcome Meson’s bottlenecks.

    Maestro was built with a distributed, scalable architecture from the ground up, capable of handling massive volumes of workflows and jobs while maintaining high reliability and low operational overhead. It was also designed to support a wide range of users and use cases, from engineers and data scientists to business analysts and content producers, making it a more versatile platform for the future of data and ML at Netflix.

    In this article, we’ll look at how Netflix designed Maestro and the challenges they faced along the way.


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    Maestro Architecture

    At the heart of Maestro's design is a microservices-based architecture that allows the system to scale efficiently.

    The system is broken down into three primary services, each responsible for a different part of the workflow orchestration. The diagram below shows this architecture in detail:

    Workflow Engine

    The Workflow Engine is the core component of Maestro. It manages the full lifecycle of workflows from the initial definition to step-by-step execution and completion. 

    In Maestro, a workflow is represented as a Directed Acyclic Graph (DAG) made up of individual units of work called steps (also referred to as jobs). These steps can include metadata, dependencies, runtime parameters, conditions, and branching logic.

    A few key responsibilities of the Workflow Engine include:

    • Managing workflow definitions, including versioning and metadata.

    • Executing workflows by managing step instances, which are the runtime versions of each step.

    • Supporting advanced data patterns like foreach loops, conditional branches, and subworkflows.

    • Allowing steps to be reused across workflows through step templates.

    • Enabling dynamic workflows by supporting code-based parameter injection through a secure custom expression language called SEL (Simple Expression Language).

    • Providing a timeline view of workflow state changes to help with debugging and tracking.

    Under the hood, the engine uses Netflix’s open-source Conductor library to manage the workflow state machine.

    Time-Based Scheduling

    Maestro supports workflows that need to be triggered at regular time intervals through the time-based scheduling service. Users can define when workflows should run using cron expressions or predefined time intervals such as hourly or weekly schedules.

    This service is lightweight and designed to scale, offering an at-least-once triggering guarantee. 

    However, the Maestro engine includes logic to deduplicate triggers, which means that even if the scheduler tries to trigger a workflow multiple times, it ensures that the workflow is only executed once. This provides exact-once execution in practice.

    Signal Service

    Time-based scheduling is especially useful for recurring tasks, but it may not always be the most efficient method. For instance, running a workflow at midnight might not make sense if the data it depends on isn’t ready yet. To address that, Maestro includes another option for triggering workflows: the signal service.

    The signal service provides support for event-driven orchestration, which complements time-based scheduling by triggering workflows or steps based on real-time events instead of a fixed time.

    A signal is essentially a small message that carries information (such as metadata or parameter values) related to a triggering event. This service plays a crucial role in enabling conditional, responsive execution. Signals can come from:

    • Internal sources, such as other workflows.

    • External systems, such as S3 events, data warehouse table updates, or other infrastructure changes.

    See the diagram below:

    The Signal Service is used in two primary ways:

    • Triggering entire workflows when a signal matches a subscribed condition.

    • Gating individual steps within a workflow, where steps can wait for a signal to arrive before they begin execution.

    In addition to these capabilities, the Signal Service also tracks signal lineage. This means it can trace which workflows were triggered by which signals, creating a dependency map between upstream and downstream workflows.

    Scalability Techniques for Maestro

    One of the core strengths of Maestro is its ability to scale horizontally and handle very large workloads without compromising reliability or performance. 

    There are a few ways Maestro achieves this:

    • At the architectural level, all of Maestro’s services are built as stateless microservices. This means that any instance of a service can handle any request, and there is no need to store session or workflow data within the service itself. Instead, all state information is stored in an external system. This design makes it easy to scale out services by simply adding more instances.

    • To handle communication between components and coordinate workflow execution, Maestro uses distributed queues. These queues decouple the services from each other, so each one can process requests at its own pace. They also allow for reliable message passing across the system, even when traffic spikes or failures occur in individual components.

    • For persistent storage of workflow definitions, step instances, and state data, Maestro uses CockroachDB. CockroachDB is a distributed SQL database that provides strong consistency guarantees. It’s well-suited for Maestro because it can scale horizontally and maintain high availability without much operational complexity. This ensures that all workflow and step data is stored reliably, even at a very large scale.

    • Another important aspect of Maestro’s scalability comes from how it handles workflows with massive numbers of steps. In many use cases, a single workflow might need to run the same job tens or even hundreds of thousands of times. For example, to backfill data over a long historical period or to train models across different parameters. Writing out all of these steps manually in a workflow definition would be impractical and hard to manage.

    • Moreover, Maestro supports nested foreach loops, meaning that one foreach block can contain another. This allows users to construct complex, large-scale workflows that execute millions or even billions of individual steps within a single workflow instance, all while remaining manageable and performant.

    See the diagram below that shows the forEach design for Maestro.

    Maestro Execution Abstractions

    Maestro is designed to serve a wide range of users from beginner data analysts to advanced engineers. Therefore, it provides multiple execution abstractions to make it easier to define and run workflows.

    Some abstractions are as follows:

    Step Types

    Maestro includes a set of predefined step types that encapsulate common tasks. 

    These step types represent standard operations, such as running a Spark job, moving data between systems (like from a database to a spreadsheet), or executing a SQL query. 

    When a user chooses a step type, they only need to supply the required parameters (such as the query string, memory allocation, or table name), and Maestro handles everything else behind the scenes.

    For example, if a user selects the "Spark" step type, they can simply provide the Spark SQL query, along with resource requirements, and Maestro will schedule and execute the job accordingly. If the internal logic for Spark jobs ever needs to be updated (such as changing how jobs are submitted), the Maestro team can make that change once, and it will automatically apply to all workflows that use that step type.

    Notebook Execution

    Maestro also supports direct execution of Jupyter notebooks, which is useful for users who prefer working in a notebook environment or want to run code that has already been developed interactively. 

    These users can pass their notebooks to Maestro, and the system will schedule and run them as part of a workflow. This is especially helpful for data scientists or analysts who may not want to write workflow logic from scratch but still need to run periodic jobs.

    Notebook execution integrates with other workflow steps, allowing users to include notebooks alongside other jobs within a larger DAG. Parameters can also be passed into notebooks dynamically, making them flexible components in a pipeline.

    Docker Jobs

    For more advanced users who need complete control over the execution environment, Maestro supports running Docker containers. These users can package their custom business logic into a Docker image, define the necessary input parameters, and let Maestro schedule and manage the container’s execution.

    This option allows users to run any logic, regardless of programming language or dependencies, as long as it is packaged inside a container.

    Maestro DSL Interface

    To make it easy for users with different technical backgrounds to define workflows, Maestro supports multiple ways to create and manage them. Users can choose from a set of Domain Specific Languages (DSLs), graphical interfaces, or programmatic APIs, depending on what works best for their needs.

    Maestro supports the following DSLs:

    • YAML: This is the most popular option because it is simple, human-readable, and easy to write. YAML is ideal for defining the structure of workflows, including steps, parameters, and dependencies, without needing to write actual code.

    • Python: For users who prefer scripting and want to dynamically generate workflows, Python DSL provides a more programmable approach. It’s useful when logic needs to be calculated or generated on the fly.

    • Java: Java DSL is available for engineers who work within Java-based systems or want to integrate workflows more closely with Java applications.

    Apart from these DSLs, users can also define workflows in the following ways:

    • API (JSON): Maestro exposes a set of APIs that allow users to submit workflow definitions directly in JSON format. This is helpful for automation or integration with other systems.

    • Maestro UI: A graphical user interface is provided for users who prefer not to write code. The UI allows users to visually create workflows by selecting and configuring steps through clicks and forms. This is especially useful for non-engineers or users who want a quick and easy way to build workflows without touching code.

    • Metaflow Integration: Maestro integrates with Metaflow, a library developed by Netflix that allows users to define DAGs in a Pythonic way. Users can write regular Python code using Metaflow and then run those workflows through Maestro. This bridges the gap between experimentation and production for data scientists.

    Parameterized Workflow Example

    A key feature of Maestro is its support for parameterized workflows, which allow workflows to adapt dynamically at runtime based on input values. This reduces duplication, simplifies maintenance, and enables powerful use cases.

    One example is a backfill window, which is commonly used to process historical data across a range of dates.

    Here’s how it works:

    • Step 1 – Compute the Date Range: The first step of the workflow uses a simple job (called a NoOp job) to calculate the list of dates that need to be backfilled. For example, it might generate all dates between 2021-01-01 and 2022-01-01. This list of dates is stored as an output parameter.

    • Step 2 – Foreach Loop Over Dates: The workflow then enters a foreach step that iterates over each date in the list produced by Step 1. For every date, it creates an iteration that runs the same set of jobs, each with a different date value as input.

    • Step 3 – Run the Backfill Job for Each Date: Inside the foreach, a job is defined to perform the actual backfill operation. In this example, it's a notebook job that runs a Jupyter notebook. The date from the current iteration is passed into the notebook as a parameter, so each run processes data for a specific day.

    See the diagram below:

    This setup allows users to create a single workflow definition that can dynamically expand into hundreds or thousands of job runs, each tailored to a different date, without having to manually write out every step.

    Aggregated View and Rollup

    When dealing with large-scale workflows, especially those that include subworkflows, foreach loops, or deeply nested execution paths, it can become difficult for users to keep track of what's happening during and after execution. To solve this, Maestro provides two helpful features: Aggregated View and Rollup.

    Aggregated Views

    The aggregated view is designed to give users a high-level summary of how a workflow has performed across multiple runs. 

    See the diagram below:

    Instead of manually going through every instance of a workflow execution, users can see a combined overview of statuses across all runs. This includes information such as how many instances:

    • Succeeded

    • Failed

    • Were skipped

    • Are still running

    This helps users quickly understand the overall behavior and health of a workflow over time, making it easier to spot patterns or recurring issues.

    Rollup

    The rollup feature goes a step further by providing a flattened summary of all the steps in a workflow, even when the workflow contains subworkflows, foreach iterations, or nested loops. 

    In large workflows, a single step can itself be a foreach loop or a call to another workflow, which then includes its steps. Rollup pulls all of this structure together into a unified view.

    With rollup, users can:

    • See the total count of steps across all levels of the workflow.

    • Track how many of those steps succeeded, failed, were skipped, or were retried.

    • Get recursive visibility, meaning it looks deep into all execution layers to give a complete picture.

    Event Publishing

    Maestro is designed not just to run workflows but also to integrate smoothly with other systems at Netflix. One of the key ways it achieves this is through a robust event publishing system, which keeps various services informed about what is happening inside the orchestration platform in real time.

    See the diagram below:

    There are two main types of events that Maestro generates.

    1 - Internal Events

    These are events that describe the internal lifecycle of a workflow. They include things like:

    • When a workflow starts

    • When a step begins execution

    • When a step or workflow completes, fails, or is retried

    • When a workflow is paused, resumed, or cancelled

    These events are used within Maestro itself and by other internal Netflix systems to track execution status, handle retries, and support user notifications or UI updates. They help ensure that the orchestration engine behaves correctly and reliably, especially under high workloads.

    2 - External Events

    Maestro can also publish events to external messaging systems such as Kafka or SNS (Simple Notification Service). 

    These events are typically consumed by other systems that need to react to changes in workflow state. For example:

    • A data warehouse system might start preparing data once it knows a data pipeline workflow has completed.

    • A monitoring system might trigger alerts if a critical workflow fails.

    • A downstream job might begin execution only after receiving confirmation that the upstream job has succeeded.

    Some examples of events published externally include:

    • Workflow definition changes, such as when a workflow is updated, versioned, or deleted.

    • Workflow instance state transitions, like when an instance moves from “scheduled” to “running” or “failed” to “succeeded.”

    By publishing these events, Maestro allows other services in the Netflix ecosystem to stay in sync with what's happening in the orchestration platform.

    Conclusion

    Maestro is Netflix’s answer to the growing complexity and scale of modern data and machine learning workflows. 

    Designed as a horizontally scalable, cloud-native orchestrator, it addresses the limitations of its predecessor, Meson, by providing a robust architecture built on stateless microservices, distributed queues, and a strong consistency layer via CockroachDB. 

    Its support for high-level execution abstractions, dynamic parameterization, and deep modularity through features like foreach, subworkflows, and reusable templates allows users to build scalable, maintainable pipelines.

    With multiple DSLs, a visual UI, and integrations like Metaflow, Maestro is accessible to a broad range of users, from engineers to analysts. Its advanced monitoring features, such as signal lineage, rollups, and event publishing, ensure transparency and real-time observability across workflows. 

    As Netflix continues to grow its data infrastructure, Maestro lays a strong, flexible foundation for the future of intelligent workflow orchestration. It has also been made open-source.

    References:


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    by "ByteByteGo" <bytebytego@substack.com> - 11:38 - 15 Apr 2025